Parallel computational optimization in operations research: A new integrative framework, literature review and research directions

TL;DR

This paper reviews recent advances in parallel optimization in operations research, proposing a new integrative framework, synthesizing prior research from 2008-2017, and outlining future research directions.

Contribution

It introduces a novel unifying framework for parallel optimization in OR and synthesizes recent literature to address fragmentation and guide future research.

Findings

Developed an integrative framework for parallel optimization

Synthesized research on parallel optimization from 2008-2017

Outlined future research directions in parallel OR

Abstract

Solving optimization problems with parallel algorithms has a long tradition in OR. Its future relevance for solving hard optimization problems in many fields, including finance, logistics, production and design, is leveraged through the increasing availability of powerful computing capabilities. Acknowledging the existence of several literature reviews on parallel optimization, we did not find reviews that cover the most recent literature on the parallelization of both exact and (meta)heuristic methods. However, in the past decade substantial advancements in parallel computing capabilities have been achieved and used by OR scholars so that an overview of modern parallel optimization in OR that accounts for these advancements is beneficial. Another issue from previous reviews results from their adoption of different foci so that concepts used to describe and structure prior literature differ. This heterogeneity is accompanied by a lack of unifying frameworks for parallel optimization across methodologies, application fields and problems, and it has finally led to an overall fragmented picture of what has been achieved and still needs to be done in parallel optimization in OR. This review addresses the aforementioned issues with three contributions: First, we suggest a new integrative framework of parallel computational optimization across optimization problems, algorithms and application domains. The framework integrates the perspectives of algorithmic design and computational implementation of parallel optimization. Second, we apply the framework to synthesize prior research on parallel optimization in OR, focusing on computational studies published in the period 2008-2017. Finally, we suggest research directions for parallel optimization in OR.